NEURODIVERSITY IN HIGHER EDUCATION: TIME SERIES DATA ANALYSIS OF THE NEPTUN UNIFIED EDUCATION SYSTEM
Bibliographic record
Abstract
This paper is linked to the current Diversity, Equality, and Inclusion research in higher education. It uses the Process Model of Inclusion to describe educational data of student groups with special needs, with a particular focus on neurodiversity at the University of Pécs (UP). It explores differences that can be identified when comparing student groups over a 10-year time-series analysis of student data from the Neptun Unified Education System (N = 47,194). Using SPSS, we explore admission rates and faculty distribution at entry, supporting factors during the process, and achievement indicators at the time of exiting university. Our results reveal that neurodivergent students appear in the largest proportion among student groups, nearly half of them receive scholarships, about a third of them acquire language proficiency exam certificates before graduation, and only a quarter of them receive dormitory placement. However, the logistic regression analysis substantiates the rather surprising outcome that neurodivergent students are the most likely to graduate successfully from UP, and they are the least likely to defer semesters during their course of study. Our macro-statistical data provide valuable starting points for our ongoing qualitative research that strives to look beyond these numbers for explanations. Keywords: Process Model of Inclusion, higher education, neurodiversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".